The use of renewable energy resources by energy providers is increasing. The estimated grid-connected photovoltaic (PV) capacity increased at an annual average rate of 60% from 2004 to 2009, and is the fastest growing energy generation technology in the world.
Increasing the PV capacity can have a major impact on reducing carbon emissions. Therefore, the use of renewable power resources including PV devices has been encouraged by world governments via taxes and subsidies. However, because the outputs of PV devices and other renewable resources are highly volatile, their increased use can also cause reliability issues.
One approach to managing this unreliability is to use a risk control module so that “blackout” due to volatility can be reduced to an acceptable level.
For a power generation system that also includes fossil burned generators, coal, natural gas, oil, diesel, one strategy to compensate for the volatility and intermittent output of the PV devices is to keep the generators operational in an idle or standby mode, and to supply a demand deficit in the event that PV generation output is suddenly reduced.
However, that strategy can actually increase the emission of greenhouse gases, because it essentially keeps some of the generators operational in certain capacity. This problem becomes more serious and less cost-effective with an increased use of the PV devices, because more and more generators need to be committed, and kept operational.
One way to deal with this problem is to predict total energy demand, and the PV output that is generated. A more accurate prediction yields smaller prediction errors that can be measured by mean absolute percentage error (MAPE), relative absolute error (RAE), mean square error (MSE), or root mean square error (RMSE). The selection of the error measures can significantly impact the selection of the prediction methods.
If the predictions are available, then the amount of the net demand, i.e., the difference between the total demand and the PV output, can be estimated accurately. The difference is then supplied by conventional fossil-burned generators.
The accurate prediction can lead to a control strategy that operates a minimal number of fossil-burning generators to meet unforeseen energy demand. Therefore, the accuracy of the prediction is crucial to cost and pollution reduction.
The prediction for PV generations has to take into account many influencing factors, including daily and annual variations, atmospheric conditions, device hardware parameters, and their complex interactions. This can be understood from the procedures by which the electricity is converted from the solar irradiance.
The PV device generates electrical power by converting solar radiation into electrical power using the photovoltaic effect of semiconductors. The photons in sunlight contain different amounts of energy corresponding to the spectrum of solar rays. PV power generation requires complex steps that depend on irradiance levels, physical conditions impacting semiconductor materials, converter delay coefficients, and short-term atmospheric fluctuations, the weather conditions, etc.
One way to predict the PV device output empirically is to characterize the current solar activity and irradiance levels to determine parameters for a PV model, and use the PV model to predict the potential output. The drawback of that approach is that the models are highly nonlinear in multiple variables, and are difficult to obtain analytically or empirically.
To avoid that drawback, an alternative approach predicts the PV outputs using statistical techniques to analyze their characteristics. The benefit of that approach is that it docs not involve the physical details of PV materials, the external atmospheric conditions, and their interactions.
One prior art method uses three statistics, namely the frequency distribution of irradiance that quantifies the fraction of time that irradiance falls within a specified range of values; the distribution of ramps, i.e., changes in level, of irradiance over a period of time; and the autocovariance and autocorrelation in the irradiance time series data.